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Chapter 3 - Benthic Habitatswere compiled from separate sources and the techniquesused to create a comprehensive map are discussed below.BathymetryA comprehensive bathymetry grid was created to characterizedepths across the region, to uncover organisms’depth preferences, and to create seabed topographicforms (Figure 3). The primary dataset used for mappingbathymetry was National Geographic Data Center’sCoastal Relief Model (CRM). The CRM is a “gridded”bathymetric surface (similar to an architect’s site model)generated from soundings of the Continental Shelf andslope. The soundings are from hydrographic surveys completedbetween 1851 and 1965, from survey data acquireddigitally on National Ocean Service (NOS) survey vesselssince 1965, and are stored in the NOS HydrographicDatabase. The CRM was prepared in a GIS format withthe value for each 82m cell representing the depth ofthat cell. In some areas, however (particularly east of theHudson Canyon), the dataset showed distinct artifacts ofinterpolation, with the resulting surface stretched into ataut plane marked with peaks and valleys at survey locationswhere actual depths were taken. In these places, datawas augmented with insets from NOS Bathymetric andFishing Maps (BFM). The BFM contours were drawnby hand, by cartographers interpreting topography fromsoundings, and provide a more credible topography insome of the problematic sections of the CRM. It shouldbe noted that a considerable data gap exists off the coast ofNorth Carolina and is reflected as an area of “no data” insubsequent analyses that rely on bathymetry (e.g., seabedforms, ecological marine units, benthic habitats).The Canadian portion of the region, including the Bay ofFundy, was covered by United States Geological Survey’s(USGS) Gulf of Maine 15’ Bathymetry (Roworth andSignell 1998). Because the spatial resolution of this layer(~350 meter cell size) is coarser than the CRM (~82 m cellsize), it was used only to fill in areas north of the Hagueline and in a section of eastern Georges Bank. A fringefrom the CRM was removed where data had been inferredup to 9 km beyond actual soundings.Seafloor Substrates: Soft Sediments and Hard BottomsSubstrate data for the entire United States portion of theregion was obtained from usSEABED, an innovative systemthat brings assorted numeric and descriptive sedimentdata together in a unified database (Reid et al. 2005). Theinformation includes textural, geophysical, and compositionalcharacteristic of points collected from the seafloor,and is spatially explicit. The data coverage extends seawardacross the Continental Shelf and slope, and combinesmore than 150 different data sources containing over200,000 data points for the Atlantic seaboard. A uniquefeature of the database is its use of data mining and processingsoftware to extend the coverage of information inareas where data coverage is more descriptive than quantitative(details in Reid et al. 2005).Initially, two standard sediment classification schemeswere experimented with - Shepard (1954) and Folk (1954)- that classify sediment types by their principal component(e.g. sand) and secondary components (e.g. muddysand). Ultimately, the average grain size of each samplewas used, which was recorded for almost every data point.To create a map of soft sediments for the region, pointswere removed from the dataset that were coded as hardbottoms (“0” in ave. grain size, and “solid” in the texturefield). Then, interpolations were generated from the remainingsediment points that ranged from 0.001 mmclays to 9 mm gravels in average size (Table 3-3).Interpolating this dataset - estimating the average grainsize for areas between the sample points - was problematicbecause there was very little spatial autocorrelation inthe average grain size of each point (Gearey’s C = 0.034,p